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Author
Last Commit
Aug. 17, 2018
Created
Sep. 24, 2017

Build Status

Spark-NLP

John Snow Labs Spark-NLP is a natural language processing library built on top of Apache Spark ML. It provides simple, performant & accurate NLP annotations for machine learning pipelines, that scale easily in a distributed environment.

Project's website

Take a look at our official spark-nlp page: http://nlp.johnsnowlabs.com/ for user documentation and examples

Slack community channel

Questions? Feedback? Request access sending an email to [email protected]

Usage

spark-packages

This library has been uploaded to the spark-packages repository https://spark-packages.org/package/JohnSnowLabs/spark-nlp .

To use the most recent version just add the --packages JohnSnowLabs:spark-nlp:1.6.1 to you spark command

spark-shell --packages JohnSnowLabs:spark-nlp:1.6.1
pyspark --packages JohnSnowLabs:spark-nlp:1.6.1
spark-submit --packages JohnSnowLabs:spark-nlp:1.6.1

Jupyter Notebook

export SPARK_HOME=/path/to/your/spark/folder
export PYSPARK_DRIVER_PYTHON=jupyter
export PYSPARK_DRIVER_PYTHON_OPTS=notebook

pyspark --packages JohnSnowLabs:spark-nlp:1.6.1

Apache Zeppelin

This way will work for both Scala and Python

export SPARK_SUBMIT_OPTIONS="--packages JohnSnowLabs:spark-nlp:1.6.1"

Alternatively, add the following Maven Coordinates to the interpreter's library list

com.johnsnowlabs.nlp:spark-nlp_2.11:1.6.1

Python without explicit Spark installation

If you installed pyspark through pip, you can now install sparknlp through pip

pip install --index-url https://test.pypi.org/simple/ spark-nlp==1.6.1

Then you'll have to create a SparkSession manually, for example:

spark = SparkSession.builder \
    .appName("ner")\
    .master("local[4]")\
    .config("spark.driver.memory","4G")\
    .config("spark.driver.maxResultSize", "2G") \
    .config("spark.driver.extraClassPath", "lib/sparknlp.jar")\
    .config("spark.kryoserializer.buffer.max", "500m")\
    .getOrCreate()

S3 Cluster with no hadoop configuration

If your distributed storage is S3 and you don't have a standard hadoop configuration (i.e. fs.defaultFS) You need to specify where in the cluster distributed storage you want to store Spark-NLP's tmp files. First, decide where you want to put your application.conf file

import com.johnsnowlabs.uti.ConfigLoader
ConfigLoader.setConfigPath("/somewhere/to/put/application.conf")

And then we need to put in such application.conf the following content

sparknlp {
  settings {
    cluster_tmp_dir = "somewhere in s3n:// path to some folder"
  }
}

Pre-compiled Spark-NLP and Spark-NLP-OCR

You may download fat-jar from here: Spark-NLP 1.6.1 FAT-JAR or non-fat from here Spark-NLP 1.6.1 PKG JAR Spark-NLP-OCR Module (Requires native Tesseract 4.x+ for image based OCR. Does not require Spark-NLP to work but highly suggested) Spark-NLP-OCR 1.6.1 FAT-JAR

Maven central

Our package is deployed to maven central. In order to add this package as a dependency in your application:

Maven

<dependency>
  <groupId>com.johnsnowlabs.nlp</groupId>
  <artifactId>spark-nlp_2.11</artifactId>
  <version>1.6.1</version>
</dependency>

SBT

libraryDependencies += "com.johnsnowlabs.nlp" % "spark-nlp_2.11" % "1.6.1"

If you are using scala 2.11

libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp" % "1.6.1"

Using the jar manually

If for some reason you need to use the jar, you can download the jar from the project's website: http://nlp.johnsnowlabs.com/

From there you can use it in your project setting the --classpath

To add jars to spark programs use the --jars option

spark-shell --jars spark-nlp.jar

The preferred way to use the library when running spark programs is using the --packages option as specified in the spark-packages section.

Downloading models for offline use

If you have troubles using pretrained() models in your environment, here a list to various models (only valid for latest versions). If there is any older than current version of a model, it means they still work for current versions.

Updated for 1.6.1

Pipelines

Models

Special community aknowledgments

Thanks in general to the community who have been lately reporting important issues and pull request with bugfixes. Community has been key in the last releases with feedback in various Spark based environments.

Here a few specific mentions for recurring feedback and slack participation

Contribute

We appreciate any sort of contributions:

  • ideas
  • feedback
  • documentation
  • bug reports
  • nlp training and testing corpora
  • development and testing

Clone the repo and submit your pull-requests! Or directly create issues in this repo.

Contact

[email protected]

John Snow Labs

http://johnsnowlabs.com/